Executive Summary

This report analyzes the performance of our optimized portfolio strategy across different market regimes and compares it against relevant benchmarks.

Portfolio Optimization Methodology

Our portfolio optimization strategy leverages advanced quantitative methods to construct liquid S&P 500 sector-diverse portfolios using principles of Markowitz Modern Portfolio Theory (MPT). The optimization framework combines cutting-edge statistical techniques with robust numerical optimization to deliver superior risk-adjusted returns.

Key Components:

1. Covariance Estimation with Ledoit-Wolf Shrinkage - We employ the Ledoit-Wolf shrinkage estimator to obtain more reliable covariance matrix estimates - This approach addresses the notorious instability of sample covariance matrices, particularly in high-dimensional settings - The shrinkage technique combines the sample covariance with a structured target matrix, reducing estimation error and improving out-of-sample performance

2. Sharpe Ratio Maximization with DEoptim - Portfolio weights are optimized using Differential Evolution (DEoptim), a robust global optimization algorithm - Our objective function maximizes the Sharpe ratio using a 4% annual risk-free rate assumption - This approach optimizes the risk-adjusted return by maximizing excess return per unit of volatility

3. Sector Diversification and Liquidity Constraints - Stock selection focuses on liquid S&P 500 constituents across all major sectors - Position sizing constraints (2-10% per holding) ensure proper diversification and risk management - Regular rebalancing (30-day frequency) maintains optimal allocation while controlling transaction costs

This methodology produces portfolios that are theoretically grounded in modern portfolio theory while being practically implementable with real-world constraints and market frictions.

Overall Performance

Overall Strategy Performance

Overall Strategy Performance

Performance by Market Regime

Bull Market Period (2018-2019)

## Loading bull returns from: daily_returns_actual_bull_20250622_161545.rds

COVID Period (2020-2021)

## Loading covid returns from: daily_returns_actual_covid_20250622_161548.rds

Recovery Period (2022-2025)

## Loading recovery returns from: daily_returns_actual_recovery_20250622_161551.rds

Detailed Performance Metrics

Detailed Performance Metrics
Metric Optimized 30-day Equal Weight SPY
Annualized Return Annualized Return 0.1681 0.1225 0.1076
Annualized Std Dev Annualized Std Dev 0.2062 0.2188 0.1998
Annualized Sharpe (Rf=0%) Annualized Sharpe (Rf=0%) 0.8152 0.5598 0.5386
Semi Deviation Semi Deviation 0.0096 0.0103 0.0093
Gain Deviation Gain Deviation 0.0088 0.0093 0.0086
Loss Deviation Loss Deviation 0.0105 0.0116 0.0105
Downside Deviation (MAR=210%) Downside Deviation (MAR=210%) 0.0139 0.0145 0.0138
Downside Deviation (Rf=0%) Downside Deviation (Rf=0%) 0.0092 0.0100 0.0091
Downside Deviation (0%) Downside Deviation (0%) 0.0092 0.0100 0.0091
Maximum Drawdown Maximum Drawdown 0.2782 0.3941 0.3573
Historical VaR (95%) Historical VaR (95%) -0.0195 -0.0204 -0.0187
Historical ES (95%) Historical ES (95%) -0.0308 -0.0339 -0.0311
Modified VaR (95%) Modified VaR (95%) -0.0194 -0.0206 -0.0188
Modified ES (95%) Modified ES (95%) -0.0385 -0.0413 -0.0364
Worst Drawdown Worst Drawdown 0.2782 0.3941 0.3573

Current Portfolio Initialization

Current Recommended Portfolio Allocation - All Positions
Ticker Company Name Sector Weight (%)
NVDA NVIDIA Corporation Technology 10.0
AAPL Apple Inc.  Technology 10.0
MSFT Microsoft Corporation Technology 9.5
GOOGL Alphabet Inc.  Technology 8.5
JPM JPMorgan Chase & Co.  Financials 8.0
XOM Exxon Mobil Corporation Energy 7.5
UNH UnitedHealth Group Health Care 7.0
NEE NextEra Energy Utilities 6.5
AMT American Tower Corp Real Estate 6.0
PLD Prologis Inc Real Estate 5.5
CAT Caterpillar Inc.  Industrials 5.0
JNJ Johnson & Johnson Health Care 4.5
WMT Walmart Inc.  Consumer Discretionary 4.0
HD Home Depot Inc.  Consumer Discretionary 3.5
PG Procter & Gamble Co.  Consumer Discretionary 3.0
KO Coca-Cola Company Consumer Discretionary 2.5
TSLA Tesla Inc.  Consumer Discretionary 2.0
VZ Verizon Communications Utilities 2.0

Conclusions

Key Findings:

1. Risk-Return Trade-off Pattern - In bull markets (2018-2019): The optimized portfolio initially underperforms SPY and equal-weight strategies - During market stress (COVID 2020): The optimized portfolio demonstrates superior downside protection - In recovery periods: The strategy captures upside while maintaining risk controls

2. This Performance Pattern Reflects Design Intent - The strategy maximizes Sharpe ratio (risk-adjusted returns) rather than absolute returns - Conservative positioning during bull markets provides “insurance” for inevitable downturns - Over full market cycles, the strategy delivers superior risk-adjusted performance

3. Practical Implications - During strong bull markets, expect some underperformance vs. market indices - The strategy’s value becomes most apparent during market corrections - Long-term investors benefit from lower drawdowns and smoother equity curves

Implementation:

Portfolio Initialization Timing - Consider market regime when initializing the portfolio - In strong bull markets, consider: - Gradual position building over 2-3 months - Higher initial allocation to growth sectors - Or accepting short-term underperformance for long-term protection

Ongoing Management - Initialize portfolio using the weights shown in the Current Portfolio section above - Rebalance every 30 days to maintain optimal allocation - Monitor regime changes for potential strategy adjustments - Consider transaction costs when implementing

Implementation Notes:

  • The current portfolio implementation uses the most recent 200 days of data
  • Stocks are selected from major sectors to ensure diversification
  • Minimum weight of 2% and maximum of 10% per position for risk management
  • Expected metrics are based on historical optimization and may vary

To generate a live portfolio recommendation with current market data:

Rscript scripts/examples/current_portfolio_recommendation.R

Report generated on 2025-06-22 using ggplot2 performance visualization



Disclaimer: This report is for informational and educational purposes only. It does not constitute financial advice. Past performance is not indicative of future results. Always consult a licensed financial advisor before making investment decisions.